Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations53794
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 MiB
Average record size in memory88.0 B

Variable types

Numeric9
Categorical1

Alerts

carat is highly overall correlated with price and 3 other fieldsHigh correlation
price is highly overall correlated with carat and 3 other fieldsHigh correlation
x is highly overall correlated with carat and 3 other fieldsHigh correlation
y is highly overall correlated with carat and 3 other fieldsHigh correlation
z is highly overall correlated with carat and 3 other fieldsHigh correlation
color has 6755 (12.6%) zeros Zeros
clarity has 740 (1.4%) zeros Zeros

Reproduction

Analysis started2025-12-25 14:19:23.507089
Analysis finished2025-12-25 14:19:32.716315
Duration9.21 seconds
Software versionydata-profiling v4.16.1
Download configurationconfig.json

Variables

carat
Real number (ℝ)

High correlation 

Distinct273
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79778005
Minimum0.2
Maximum5.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size840.5 KiB
2025-12-25T16:19:32.812586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.4
median0.7
Q31.04
95-th percentile1.7
Maximum5.01
Range4.81
Interquartile range (IQR)0.64

Descriptive statistics

Standard deviation0.47339024
Coefficient of variation (CV)0.5933844
Kurtosis1.2471577
Mean0.79778005
Median Absolute Deviation (MAD)0.32
Skewness1.1136511
Sum42915.78
Variance0.22409832
MonotonicityNot monotonic
2025-12-25T16:19:33.163182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 2596
 
4.8%
1.01 2240
 
4.2%
0.31 2238
 
4.2%
0.7 1981
 
3.7%
0.32 1827
 
3.4%
1 1548
 
2.9%
0.9 1484
 
2.8%
0.41 1378
 
2.6%
0.71 1292
 
2.4%
0.4 1291
 
2.4%
Other values (263) 35919
66.8%
ValueCountFrequency (%)
0.2 12
 
< 0.1%
0.21 9
 
< 0.1%
0.22 5
 
< 0.1%
0.23 293
0.5%
0.24 254
0.5%
0.25 212
0.4%
0.26 253
0.5%
0.27 233
0.4%
0.28 198
0.4%
0.29 130
0.2%
ValueCountFrequency (%)
5.01 1
< 0.1%
4.5 1
< 0.1%
4.13 1
< 0.1%
4.01 2
< 0.1%
4 1
< 0.1%
3.67 1
< 0.1%
3.65 1
< 0.1%
3.51 1
< 0.1%
3.5 1
< 0.1%
3.4 1
< 0.1%

cut
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size840.5 KiB
2
21488 
3
13748 
4
12069 
1
4891 
0
 
1598

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters53794
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
2 21488
39.9%
3 13748
25.6%
4 12069
22.4%
1 4891
 
9.1%
0 1598
 
3.0%

Length

2025-12-25T16:19:33.279402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-25T16:19:33.381430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 21488
39.9%
3 13748
25.6%
4 12069
22.4%
1 4891
 
9.1%
0 1598
 
3.0%

Most occurring characters

ValueCountFrequency (%)
2 21488
39.9%
3 13748
25.6%
4 12069
22.4%
1 4891
 
9.1%
0 1598
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21488
39.9%
3 13748
25.6%
4 12069
22.4%
1 4891
 
9.1%
0 1598
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21488
39.9%
3 13748
25.6%
4 12069
22.4%
1 4891
 
9.1%
0 1598
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21488
39.9%
3 13748
25.6%
4 12069
22.4%
1 4891
 
9.1%
0 1598
 
3.0%

color
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5939138
Minimum0
Maximum6
Zeros6755
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size840.5 KiB
2025-12-25T16:19:33.472158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7011165
Coefficient of variation (CV)0.65581074
Kurtosis-0.86640993
Mean2.5939138
Median Absolute Deviation (MAD)1
Skewness0.19000596
Sum139537
Variance2.8937975
MonotonicityNot monotonic
2025-12-25T16:19:33.566765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 11262
20.9%
1 9776
18.2%
2 9520
17.7%
4 8272
15.4%
0 6755
12.6%
5 5407
10.1%
6 2802
 
5.2%
ValueCountFrequency (%)
0 6755
12.6%
1 9776
18.2%
2 9520
17.7%
3 11262
20.9%
4 8272
15.4%
5 5407
10.1%
6 2802
 
5.2%
ValueCountFrequency (%)
6 2802
 
5.2%
5 5407
10.1%
4 8272
15.4%
3 11262
20.9%
2 9520
17.7%
1 9776
18.2%
0 6755
12.6%

clarity
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8357624
Minimum0
Maximum7
Zeros740
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size840.5 KiB
2025-12-25T16:19:33.657262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7248716
Coefficient of variation (CV)0.44968155
Kurtosis-0.82131591
Mean3.8357624
Median Absolute Deviation (MAD)1
Skewness0.17416379
Sum206341
Variance2.9751819
MonotonicityNot monotonic
2025-12-25T16:19:33.754014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 13032
24.2%
5 12229
22.7%
3 9150
17.0%
4 8156
15.2%
7 5056
 
9.4%
6 3647
 
6.8%
1 1784
 
3.3%
0 740
 
1.4%
ValueCountFrequency (%)
0 740
 
1.4%
1 1784
 
3.3%
2 13032
24.2%
3 9150
17.0%
4 8156
15.2%
5 12229
22.7%
6 3647
 
6.8%
7 5056
 
9.4%
ValueCountFrequency (%)
7 5056
 
9.4%
6 3647
 
6.8%
5 12229
22.7%
4 8156
15.2%
3 9150
17.0%
2 13032
24.2%
1 1784
 
3.3%
0 740
 
1.4%

depth
Real number (ℝ)

Distinct184
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.74808
Minimum43
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size840.5 KiB
2025-12-25T16:19:33.871018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile59.3
Q161
median61.8
Q362.5
95-th percentile63.8
Maximum79
Range36
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.4299091
Coefficient of variation (CV)0.023157143
Kurtosis5.4129891
Mean61.74808
Median Absolute Deviation (MAD)0.7
Skewness-0.11425026
Sum3321676.2
Variance2.0446401
MonotonicityNot monotonic
2025-12-25T16:19:34.004385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 2233
 
4.2%
61.9 2160
 
4.0%
61.8 2069
 
3.8%
62.2 2033
 
3.8%
62.1 2011
 
3.7%
61.6 1950
 
3.6%
62.3 1931
 
3.6%
61.7 1899
 
3.5%
62.4 1787
 
3.3%
61.5 1717
 
3.2%
Other values (174) 34004
63.2%
ValueCountFrequency (%)
43 2
< 0.1%
44 1
< 0.1%
50.8 1
< 0.1%
51 1
< 0.1%
52.2 1
< 0.1%
52.3 1
< 0.1%
52.7 1
< 0.1%
53 1
< 0.1%
53.1 1
< 0.1%
53.2 2
< 0.1%
ValueCountFrequency (%)
79 1
< 0.1%
78.2 1
< 0.1%
73.6 1
< 0.1%
72.9 1
< 0.1%
72.2 1
< 0.1%
71.8 1
< 0.1%
71.6 2
< 0.1%
71.3 1
< 0.1%
71.2 1
< 0.1%
71 1
< 0.1%

table
Real number (ℝ)

Distinct127
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.458109
Minimum43
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size840.5 KiB
2025-12-25T16:19:34.137715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile54
Q156
median57
Q359
95-th percentile61
Maximum95
Range52
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2336791
Coefficient of variation (CV)0.038874915
Kurtosis2.7753598
Mean57.458109
Median Absolute Deviation (MAD)1
Skewness0.79222729
Sum3090901.5
Variance4.9893222
MonotonicityNot monotonic
2025-12-25T16:19:34.260875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 9851
18.3%
57 9695
18.0%
58 8352
15.5%
59 6562
12.2%
55 6242
11.6%
60 4232
7.9%
54 2583
 
4.8%
61 2277
 
4.2%
62 1267
 
2.4%
63 587
 
1.1%
Other values (117) 2146
 
4.0%
ValueCountFrequency (%)
43 1
 
< 0.1%
44 1
 
< 0.1%
49 2
 
< 0.1%
50 2
 
< 0.1%
50.1 1
 
< 0.1%
51 9
 
< 0.1%
51.6 1
 
< 0.1%
52 56
0.1%
52.4 1
 
< 0.1%
52.8 2
 
< 0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
79 1
 
< 0.1%
76 1
 
< 0.1%
73 3
 
< 0.1%
71 1
 
< 0.1%
70 9
 
< 0.1%
69 9
 
< 0.1%
68 21
 
< 0.1%
67 42
0.1%
66 91
0.2%

price
Real number (ℝ)

High correlation 

Distinct11602
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3933.0651
Minimum326
Maximum18823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size840.5 KiB
2025-12-25T16:19:34.380262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum326
5-th percentile544
Q1951
median2401
Q35326.75
95-th percentile13100.05
Maximum18823
Range18497
Interquartile range (IQR)4375.75

Descriptive statistics

Standard deviation3988.1145
Coefficient of variation (CV)1.0139966
Kurtosis2.1784531
Mean3933.0651
Median Absolute Deviation (MAD)1670
Skewness1.6182399
Sum2.115753 × 108
Variance15905057
MonotonicityNot monotonic
2025-12-25T16:19:34.507149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
605 132
 
0.2%
802 126
 
0.2%
625 125
 
0.2%
776 124
 
0.2%
828 124
 
0.2%
698 121
 
0.2%
544 120
 
0.2%
789 120
 
0.2%
552 113
 
0.2%
666 112
 
0.2%
Other values (11592) 52577
97.7%
ValueCountFrequency (%)
326 2
< 0.1%
327 1
< 0.1%
334 1
< 0.1%
335 1
< 0.1%
336 2
< 0.1%
337 2
< 0.1%
338 1
< 0.1%
339 1
< 0.1%
340 1
< 0.1%
342 1
< 0.1%
ValueCountFrequency (%)
18823 1
< 0.1%
18818 1
< 0.1%
18806 1
< 0.1%
18804 1
< 0.1%
18803 1
< 0.1%
18797 1
< 0.1%
18795 2
< 0.1%
18791 2
< 0.1%
18788 1
< 0.1%
18787 1
< 0.1%

x
Real number (ℝ)

High correlation 

Distinct554
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7312144
Minimum0
Maximum10.74
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size840.5 KiB
2025-12-25T16:19:34.636444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.29
Q14.71
median5.7
Q36.54
95-th percentile7.66
Maximum10.74
Range10.74
Interquartile range (IQR)1.83

Descriptive statistics

Standard deviation1.1206947
Coefficient of variation (CV)0.19554228
Kurtosis-0.62906812
Mean5.7312144
Median Absolute Deviation (MAD)0.92
Skewness0.37962391
Sum308304.95
Variance1.2559567
MonotonicityNot monotonic
2025-12-25T16:19:34.764954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.37 444
 
0.8%
4.34 436
 
0.8%
4.33 428
 
0.8%
4.38 426
 
0.8%
4.32 422
 
0.8%
4.35 406
 
0.8%
4.39 387
 
0.7%
4.31 384
 
0.7%
4.36 384
 
0.7%
4.4 368
 
0.7%
Other values (544) 49709
92.4%
ValueCountFrequency (%)
0 7
< 0.1%
3.73 2
 
< 0.1%
3.74 1
 
< 0.1%
3.76 1
 
< 0.1%
3.77 1
 
< 0.1%
3.79 2
 
< 0.1%
3.81 3
< 0.1%
3.82 2
 
< 0.1%
3.83 3
< 0.1%
3.84 4
< 0.1%
ValueCountFrequency (%)
10.74 1
< 0.1%
10.23 1
< 0.1%
10.14 1
< 0.1%
10.02 1
< 0.1%
10.01 1
< 0.1%
10 1
< 0.1%
9.86 1
< 0.1%
9.66 1
< 0.1%
9.65 1
< 0.1%
9.54 1
< 0.1%

y
Real number (ℝ)

High correlation 

Distinct552
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7346533
Minimum0
Maximum58.9
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size840.5 KiB
2025-12-25T16:19:34.886621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.3
Q14.72
median5.71
Q36.54
95-th percentile7.64
Maximum58.9
Range58.9
Interquartile range (IQR)1.82

Descriptive statistics

Standard deviation1.1412092
Coefficient of variation (CV)0.1990023
Kurtosis91.752305
Mean5.7346533
Median Absolute Deviation (MAD)0.92
Skewness2.445768
Sum308489.94
Variance1.3023584
MonotonicityNot monotonic
2025-12-25T16:19:35.013586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.34 436
 
0.8%
4.37 432
 
0.8%
4.35 424
 
0.8%
4.33 420
 
0.8%
4.32 411
 
0.8%
4.39 406
 
0.8%
4.38 404
 
0.8%
4.31 384
 
0.7%
4.41 383
 
0.7%
4.4 381
 
0.7%
Other values (542) 49713
92.4%
ValueCountFrequency (%)
0 6
< 0.1%
3.68 1
 
< 0.1%
3.71 2
 
< 0.1%
3.72 1
 
< 0.1%
3.73 1
 
< 0.1%
3.75 1
 
< 0.1%
3.77 2
 
< 0.1%
3.78 5
< 0.1%
3.8 1
 
< 0.1%
3.81 1
 
< 0.1%
ValueCountFrequency (%)
58.9 1
< 0.1%
31.8 1
< 0.1%
10.54 1
< 0.1%
10.16 1
< 0.1%
10.1 1
< 0.1%
9.94 2
< 0.1%
9.85 1
< 0.1%
9.81 1
< 0.1%
9.63 1
< 0.1%
9.59 1
< 0.1%

z
Real number (ℝ)

High correlation 

Distinct375
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5387136
Minimum0
Maximum31.8
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size840.5 KiB
2025-12-25T16:19:35.139460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.65
Q12.91
median3.53
Q34.03
95-th percentile4.73
Maximum31.8
Range31.8
Interquartile range (IQR)1.12

Descriptive statistics

Standard deviation0.70503748
Coefficient of variation (CV)0.19923553
Kurtosis47.383974
Mean3.5387136
Median Absolute Deviation (MAD)0.57
Skewness1.5290234
Sum190361.56
Variance0.49707785
MonotonicityNot monotonic
2025-12-25T16:19:35.268083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 761
 
1.4%
2.69 745
 
1.4%
2.71 733
 
1.4%
2.68 730
 
1.4%
2.72 691
 
1.3%
2.67 647
 
1.2%
2.73 605
 
1.1%
2.66 553
 
1.0%
2.74 545
 
1.0%
4.02 538
 
1.0%
Other values (365) 47246
87.8%
ValueCountFrequency (%)
0 19
< 0.1%
1.07 1
 
< 0.1%
1.41 1
 
< 0.1%
1.53 1
 
< 0.1%
2.06 1
 
< 0.1%
2.24 1
 
< 0.1%
2.25 1
 
< 0.1%
2.26 1
 
< 0.1%
2.27 1
 
< 0.1%
2.28 1
 
< 0.1%
ValueCountFrequency (%)
31.8 1
< 0.1%
8.06 1
< 0.1%
6.98 1
< 0.1%
6.72 1
< 0.1%
6.43 1
< 0.1%
6.38 1
< 0.1%
6.31 1
< 0.1%
6.27 1
< 0.1%
6.24 1
< 0.1%
6.17 1
< 0.1%

Interactions

2025-12-25T16:19:31.582772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:24.318282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:25.231425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:26.259888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:27.147768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:28.035779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:28.866585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:29.893716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:30.731948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:31.673200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:24.418209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:25.334418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:26.353305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:27.253596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:28.125112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:29.113150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:29.981902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:30.821983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:31.766947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:24.539538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:25.433002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:26.447404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:27.354658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:28.215806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:29.210675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:30.074432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:30.916822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:31.866202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:24.648377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:25.542820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:26.547488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:27.454273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:28.312668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:29.310901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:30.172227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:31.015810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:31.965281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:24.758823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:25.791431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:26.649434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:27.551472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:28.407541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:29.413827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:30.268977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:31.117766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:32.054607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:24.852866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:25.884196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:26.745589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:27.643209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:28.495207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:29.506154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:30.356168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:31.206844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:32.154877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:24.954592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:25.983674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:26.848296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:27.745327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:28.591844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:29.605494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:30.452123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:31.308168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:32.243243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:25.045147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:26.073779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:26.949404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:27.840687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:28.679969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:29.699944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:30.542388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:31.398297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:32.335179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:25.138012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:26.167150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:27.048030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:27.937274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:28.772788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:29.795375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:30.635883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-25T16:19:31.491323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-12-25T16:19:35.361743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
caratclaritycolorcutdepthpricetablexyz
carat1.000-0.2160.2490.1150.0300.9630.1940.9960.9960.993
clarity-0.2161.000-0.0230.142-0.052-0.116-0.085-0.214-0.212-0.217
color0.249-0.0231.0000.0360.0490.1500.0280.2450.2450.251
cut0.1150.1420.0361.0000.4050.0930.2900.1480.1070.096
depth0.030-0.0520.0490.4051.0000.010-0.245-0.023-0.0250.104
price0.963-0.1160.1500.0930.0101.0000.1710.9630.9630.957
table0.194-0.0850.0280.290-0.2450.1711.0000.2010.1950.159
x0.996-0.2140.2450.148-0.0230.9630.2011.0000.9980.987
y0.996-0.2120.2450.107-0.0250.9630.1950.9981.0000.987
z0.993-0.2170.2510.0960.1040.9570.1590.9870.9871.000

Missing values

2025-12-25T16:19:32.456912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-25T16:19:32.617662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

caratcutcolorclaritydepthtablepricexyz
00.2321361.555.03263.953.982.43
10.2131259.861.03263.893.842.31
20.2311456.965.03274.054.072.31
30.2935562.458.03344.204.232.63
40.3116363.358.03354.344.352.75
50.2446762.857.03363.943.962.48
60.2445662.357.03363.953.982.47
70.2644261.955.03374.074.112.53
80.2201565.161.03373.873.782.49
90.2344459.461.03384.004.052.39
caratcutcolorclaritydepthtablepricexyz
539300.7131260.555.027565.795.743.49
539310.7132259.862.027565.745.733.43
539320.7041560.559.027575.715.763.47
539330.7041561.259.027575.695.723.49
539340.7230262.759.027575.695.733.58
539350.7220260.857.027575.755.763.50
539360.7210263.155.027575.695.753.61
539370.7040262.860.027575.665.683.56
539380.8634361.058.027576.156.123.74
539390.7520362.255.027575.835.873.64